%0 Journal Article
%T Enhancement the wastewater treatment performance of multistage living machine by underwater lamp.
%A Chen R
%A Li T
%A Huang G
%A Jia X
%A Jin Z
%A Zheng X
%A Zhao M
%J J Environ Manage
%V 365
%N 0
%D 2024 Aug 26
%M 38936021
%F 8.91
%R 10.1016/j.jenvman.2024.121604
%X Source separation and decentralized domestic wastewater treatment represent effective strategies to enhance sewage treatment performance and facilitate water reuse economically. The Living Machine (LM) system has gained widespread adoption for decentralized sewage treatment. While underwater light source has been demonstrated to enhance the treatment performance of open aerobic reactors in LM systems, its influence on the treatment efficiency of a fully multistage LM system remains underreported. In this study, an underwater lamp-added LM system (ULLM) with eight reactors was constructed and investigated. The introduction of underwater light source obviously improved the removal capacity of chemical oxygen demand (COD) and NH4+-N, which was 96.1% and 61.6%, respectively. The diversity of algae, zooplankton, and aquatic animals was notably higher in the light-treated reactors than in the control group (CK) without underwater light source, and substantial alteration in the microbial community of the light-treated reactors was observed compared with CK reactors. At the phylum level, Proteobacteria and Nitrospirae enriched in the underwater light-treated reactors, while Bacteroidetes and Actinobacteria exhibited a decrease after light exposure. At the genus level, Nitrospira and Rhodanobacter were enriched in the ULLM system. Importantly, the prevalence of these two dominant genera was sustained until the final operational stage, indicating their potential key roles in enhancing wastewater treatment performance. The addition of underwater light source proves to be an effective strategy for augmenting the treatment efficiency of the multistage living machine systems, resulting in substantial improvements in pollutant removal. These findings contribute valuable insights into optimizing LM systems for decentralized wastewater treatment.